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Found 6 papers, 1 papers with code
RedEx: Beyond Fixed Representation Methods via Convex Optimization
Optimizing Neural networks is a difficult task which is still not well understood.
Locally Optimal Descent for Dynamic Stepsize Scheduling
We introduce a novel dynamic learning-rate scheduling scheme grounded in theory with the goal of simplifying the manual and time-consuming tuning of schedules in practice.
The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models
Our results reveal a connection between human language processing and DLMs, with the DLM's layer-by-layer accumulation of contextual information mirroring the timing of neural activity in high-order language areas.
Adversarial Robustness of Streaming Algorithms through Importance Sampling
In this paper, we introduce adversarially robust streaming algorithms for central machine learning and algorithmic tasks, such as regression and clustering, as well as their more general counterparts, subspace embedding, low-rank approximation, and coreset construction.
Asynchronous Stochastic Optimization Robust to Arbitrary Delays
In the general non-convex smooth optimization setting, we give a simple and efficient algorithm that requires $O( \sigma^2/\epsilon^4 + \tau/\epsilon^2 )$ steps for finding an $\epsilon$-stationary point $x$, where $\tau$ is the \emph{average} delay $\smash{\frac{1}{T}\sum_{t=1}^T d_t}$ and $\sigma^2$ is the variance of the stochastic gradients.
Scalable Learning of Non-Decomposable Objectives
Modern retrieval systems are often driven by an underlying machine learning model.
